Participatory Analytics & Visualisations to Make Sense of Urban Data
QUT Signature Project 2015 – 2016
In recent years we have seen a significant increase in the volume and accessibility of (big) urban data sets that describe many aspects of urban living. One of the major challenges in the analysis of urban data is to understand the complex interrelations between different data sets within particular contexts. While this task is generally approached using traditional methods of data analysis, such means of study do not generally support the exploratory and serendipitous discovery of meaning. Visual analytics is a field of research that uses information visualisation techniques to support teams of experts in the exploration and collaborative analysis of data. Visual analytics is increasingly applied in the study of urban environments, for instance in the analysis of geographic phenomena in urban areas.
In this project we aim to explore means that allow everyday citizens to visualise and make use of urban data through the development and study of a data-exploration and visual analytics framework targeted at lay users. We argue that providing citizens with means to visualise key aspects of their urban environment such as energy use, commuting options or property development patterns in their neighbourhood can help making big urban data accessible to everyday citizens. Similarly, a tool that supports the visual analysis of urban data can become an effective communication tool, facilitating the discussions between urban planners and citizens and allowing them to mutually explain and critique planning decisions. Lastly, the use of a visual analytic platforms opens up the possibility that gained insights can be shared with other users. Such a platform could be seen as a crowd-sourcing tool that utilises the local knowledge of citizens to attribute meaning to (big) urban data sources.
Team
- Dr Markus Rittenbruch
- Professor Marcus Foth
- Prof. Robin Drogemuller
- Assoc. Prof. Dian Tjondronegoro
- Irina Anastasiu
Partners
